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| import streamlit as st | |
| import outetts | |
| from scipy.io.wavfile import write | |
| import tempfile | |
| import os | |
| from pydub import AudioSegment | |
| import sounddevice as sd | |
| import wave | |
| import numpy as np | |
| # Initialize model configuration | |
| model_config = outetts.HFModelConfig_v1( | |
| model_path="OuteAI/OuteTTS-0.2-500M", | |
| language="en" # Supported languages: en, zh, ja, ko | |
| ) | |
| # Initialize the interface | |
| interface = outetts.InterfaceHF(model_version="0.2", cfg=model_config) | |
| # Streamlit UI | |
| st.title("OuteTTS Speech Synthesis") | |
| st.write("Enter text below to generate speech.") | |
| # Sidebar for reference voice | |
| st.sidebar.title("Voice Cloning") | |
| reference_audio = st.sidebar.file_uploader("Upload a reference audio (any format)", type=["wav", "mp3", "ogg", "flac", "m4a"]) | |
| transcript = st.sidebar.text_area("Transcription of the reference audio") | |
| # Function to convert audio to WAV format | |
| def convert_to_wav(audio_file): | |
| temp_audio = tempfile.NamedTemporaryFile(delete=False, suffix=".wav") | |
| audio = AudioSegment.from_file(audio_file) | |
| audio.export(temp_audio.name, format="wav") | |
| return temp_audio.name | |
| if reference_audio and transcript: | |
| ref_audio_path = convert_to_wav(reference_audio) | |
| # Create speaker profile | |
| speaker = interface.create_speaker(ref_audio_path, transcript) | |
| # Save the speaker profile | |
| interface.save_speaker(speaker, "speaker.json") | |
| else: | |
| speaker = None | |
| # Recording functionality | |
| def record_audio(duration=5, samplerate=44100): | |
| st.sidebar.write("Recording...") | |
| recording = sd.rec(int(duration * samplerate), samplerate=samplerate, channels=1, dtype=np.int16) | |
| sd.wait() | |
| temp_audio_path = tempfile.NamedTemporaryFile(delete=False, suffix=".wav").name | |
| with wave.open(temp_audio_path, "wb") as wf: | |
| wf.setnchannels(1) | |
| wf.setsampwidth(2) | |
| wf.setframerate(samplerate) | |
| wf.writeframes(recording.tobytes()) | |
| return temp_audio_path | |
| if not speaker: | |
| st.sidebar.write("Or record your voice below:") | |
| if st.sidebar.button("Record Voice"): | |
| ref_audio_path = record_audio() | |
| st.sidebar.success("Recording complete!") | |
| transcript = st.sidebar.text_area("Transcription of the recorded audio") | |
| if transcript: | |
| # Create speaker profile from recorded audio | |
| speaker = interface.create_speaker(ref_audio_path, transcript) | |
| # Save the speaker profile | |
| interface.save_speaker(speaker, "speaker.json") | |
| text_input = st.text_area("Text to convert to speech:", "Hello, this is an AI-generated voice.") | |
| if st.button("Generate Speech"): | |
| with st.spinner("Generating audio..."): | |
| # Generate speech with or without the speaker profile | |
| output = interface.generate( | |
| text=text_input, | |
| temperature=0.1, | |
| repetition_penalty=1.1, | |
| max_length=4096, | |
| speaker=speaker | |
| ) | |
| # Save the synthesized speech to a file | |
| output_path = "output.wav" | |
| output.save(output_path) | |
| # Play the audio in the Streamlit app | |
| st.audio(output_path, format="audio/wav") | |
| st.success("Speech generated successfully!") | |
| # Clean up temporary files | |
| if reference_audio: | |
| os.remove(ref_audio_path) | |